Plotting in R with ggplot2

Graphics in R

The R language has extensive graphical capabilities.

Graphics in R may be created by many different methods including base graphics and more advanced plotting packages such as lattice.

ggplot2

The ggplot2 package was created by Hadley Wickham and provides a intuitive plotting system to rapidly generate publication quality graphics.

ggplot2 builds on the concept of the “Grammar of Graphics” (Wilkinson 2005, Bertin 1983) which describes a consistent syntax for the construction of a wide range of complex graphics by a concise description of their components.

ggplot2 is a core part of the tidyverse, a group of packages designed to make data science easy and functional in R. For an introduction to these concepts check out our tidy workshop.

Why use ggplot2

The structured syntax and high level of abstraction used by ggplot2 should allow for the user to concentrate on the vizualisations instead of creating the underlying code.

On top of this central philosophy ggplot2 has:

Increased flexible over many plotting systems.
An advanced theme system for professional/publication level graphics.
Large developer base – Many libraries extending its flexibility.
Large user base – Great documentation and active mailing list.

Grammar of Graphics

How ggplot2 builds a plot

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Example scatter plot

Overview of example code for the ggplot2 scatter plot.

ggplot(data = <default data set>, 
       aes(x = <default x axis variable>,
           y = <default y axis variable>,
           ... <other default aesthetic mappings>),
       ... <other plot defaults>) +

       geom_scatter(aes(size = <size variable for this geom>, 
                      ... <other aesthetic mappings>),
                  data = <data for this point geom>,
                  stat = <statistic string or function>,
                  position = <position string or function>,
                  color = <"fixed color specification">,
                  <other arguments, possibly passed to the _stat_ function) +

  scale_<aesthetic>_<type>(name = <"scale label">,
                     breaks = <where to put tick marks>,
                     labels = <labels for tick marks>,
                     ... <other options for the scale>) +
  
  ggtitle("Graphics/Plot")+
  xlab("Weight")+
  ylab("Height")+

  theme(plot.title = element_text(color = "gray"),
        ... <other theme elements>)

What users are required to specify

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Actual example scatter plot

ggplot(data = patients_clean, aes(y = Weight, x = Height, color = Sex, size = BMI, shape = Pet)) + geom_point()

Getting Started With ggplot2

Getting started with ggplot2

Our dataset has a variety of variables.

library(ggplot2)

patients_clean <- read.delim("data/patient-data-cleaned.txt", sep = "\t")

colnames(patients_clean)

##  [1] "ID"                 "Name"               "Race"               "Sex"               
##  [5] "Smokes"             "Height"             "Weight"             "Birth"             
##  [9] "State"              "Pet"                "Grade"              "Died"              
## [13] "Count"              "Date.Entered.Study" "Age"                "BMI"               
## [17] "Overweight"

nrow(patients_clean)

## [1] 100

Our first ggplot2 graph

As seen above, in order to produce a ggplot2 graph we need a minimum of:

Data to be used in graph
Mappings of data to the graph (aesthetic mapping)
What type of graph we want to use (The geom to use).

Our first ggplot2 graph

In the code below we define the data as our cleaned patients data frame.

pcPlot <- ggplot(data = patients_clean)
class(pcPlot)

## [1] "gg"     "ggplot"

pcPlot$data[1:4, ]

##          ID    Name  Race  Sex     Smokes Height Weight      Birth        State  Pet Grade  Died Count
## 1 AC/AH/001 Michael White Male Non-Smoker 182.87  76.57 1972-02-06      Georgia  Dog     2 FALSE  0.01
## 2 AC/AH/017   Derek White Male Non-Smoker 179.12  80.43 1972-06-15     Missouri  Dog     2 FALSE -1.31
## 3 AC/AH/020    Todd Black Male Non-Smoker 169.15  75.48 1972-07-09 Pennsylvania None     2 FALSE -0.17
## 4 AC/AH/022  Ronald White Male Non-Smoker 175.66  94.54 1972-08-17      Florida  Cat     1 FALSE -1.10
##   Date.Entered.Study Age   BMI Overweight
## 1         2015-12-01  44 22.90      FALSE
## 2         2015-12-01  43 25.07       TRUE
## 3         2015-12-01  43 26.38       TRUE
## 4         2015-12-01  43 30.64       TRUE

Now we can see that we have gg/ggplot object (pcPlot).

Our first ggplot2 graph

Within this gg/ggplot object the data has been defined.

Our first ggplot2 graph

Important information on how to map the data to the vizual properties (aesthetics) of the plot as well as what type of plot to use (geom) have however yet to specified.

pcPlot$mapping

## Aesthetic mapping: 
## <empty>

pcPlot$theme

## list()

pcPlot$layers

## list()

Our first ggplot2 graph

The information to map the data to the plot can be added now using the aes() function.

pcPlot <- ggplot(data = patients_clean)

pcPlot <- pcPlot + aes(x = Height, y = Weight)

pcPlot$mapping

## Aesthetic mapping: 
## * `x` -> `Height`
## * `y` -> `Weight`

pcPlot$theme

## list()

pcPlot$layers

## list()

But we are still missing the final component of our plot, the type of plot to use (geom).

Our first ggplot2 graph

Below the geom_point function is used to specify a point plot, a scatter plot of Height values on the x-axis versus Weight values on the y values.

pcPlot <- ggplot(data = patients_clean)

pcPlot <- pcPlot + aes(x = Height, y = Weight)
pcPlot <- pcPlot + geom_point()
pcPlot

pcPlot$mapping

## Aesthetic mapping: 
## * `x` -> `Height`
## * `y` -> `Weight`

pcPlot$theme

## list()

pcPlot$layers

## [[1]]
## geom_point: na.rm = FALSE
## stat_identity: na.rm = FALSE
## position_identity

Our first ggplot2 graph

Now we have all the components of our plot, we need we can display the results.

pcPlot

Our first ggplot2 graph

More typically, the data and aesthetics are defined within ggplot function and geoms applied afterwards.

pcPlot <- ggplot(data = patients_clean, mapping = aes(x = Height, y = Weight))
pcPlot + geom_point()

Geoms

Plot types

As we have seen, an important element of a ggplot is the geom used. Following the specification of data, the geom describes the type of plot used.

Several geoms are available in ggplot2:

geom_point() - Scatter plots
geom_line() - Line plots
geom_smooth() - Fitted line plots
geom_bar() - Bar plots
geom_boxplot() - Boxplots
geom_jitter() - Jitter to plots
geom_histogram() - Histogram plots
geom_density() - Density plots
geom_text() - Text to plots
geom_errorbar() - Errorbars to plots
geom_violin() - Violin plots

Line plots

pcPlot <- ggplot(data = patients_clean, mapping = aes(x = Height, y = Weight))

pcPlot_line <- pcPlot + geom_line()

pcPlot_line

Smoothed line plots

pcPlot <- ggplot(data = patients_clean, mapping = aes(x = Height, y = Weight))

pcPlot_smooth <- pcPlot + geom_smooth()

pcPlot_smooth

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Bar plots

pcPlot <- ggplot(data = patients_clean, mapping = aes(x = Sex))

pcPlot_bar <- pcPlot + geom_bar()

pcPlot_bar

Histograms

pcPlot <- ggplot(data = patients_clean, mapping = aes(x = Height))

pcPlot_hist <- pcPlot + geom_histogram()

pcPlot_hist

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Density plots

pcPlot <- ggplot(data = patients_clean, mapping = aes(x = Height))

pcPlot_density <- pcPlot + geom_density()

pcPlot_density

Box plots

pcPlot <- ggplot(data = patients_clean, mapping = aes(x = Sex, y = Height))

pcPlot_boxplot <- pcPlot + geom_boxplot()

pcPlot_boxplot

GViolin plots

pcPlot <- ggplot(data = patients_clean, mapping = aes(x = Sex, y = Height))

pcPlot_violin <- pcPlot + geom_violin()

pcPlot_violin

There are a world of geoms

An overview of geoms and thier arguments can be found at ggplot2 documentation or within the ggplot2 cheatsheet.

Aesthetics

In order to change the property on an aesthetic of a plot into a constant value (e.g. set color of all points to red) we can supply the color argument to the geom_point() function.

pcPlot <- ggplot(data = patients_clean, mapping = aes(x = Height, y = Weight))
pcPlot + geom_point(color = "red")

Plot properties

As we discussed earlier however, ggplot2 makes use of aesthetic mappings to assign variables in the data to the properties/aesthetics of the plot. This allows the properties of the plot to reflect variables in the data dynamically.

In these examples we supply additional information to the aes() function to define what information to display and how it is represented in the plot.

First we can recreate the plot we saw earlier.

pcPlot <- ggplot(data=patients_clean,
                 mapping=aes(x=Height,
                             y=Weight))
pcPlot+geom_point()

Color

Now we can adjust the aes mapping by supplying an argument to the color parameter in the aes function. (Note that ggplot2 accepts “color” or “color” as parameter name)

This simple adjustment allows for identification of the separation between male and female measurements for height and weight.

pcPlot <- ggplot(data = patients_clean, mapping = aes(x = Height, y = Weight, color = Sex))
pcPlot + geom_point()

Point shape

Similarly the shape of points may be adjusted.

pcPlot <- ggplot(data = patients_clean, mapping = aes(x = Height, y = Weight, shape = Sex))
pcPlot + geom_point()

Aesthetics in geom

The aesthetic mappings may be set directly in the geom_points() function as previously when specifying red. This can allow the same ggplot object to be used by different aesethetic mappings and varying geoms

pcPlot <- ggplot(data = patients_clean)
pcPlot + geom_point(aes(x = Height, y = Weight, color = Sex))

pcPlot + geom_point(aes(x = Height, y = Weight, color = Smokes))

pcPlot + geom_point(aes(x = Height, y = Weight, color = Smokes, shape = Sex))

pcPlot + geom_violin(aes(x = Sex, y = Height, fill = Smokes))

Aesthetics in geom

Again, for a comprehensive list of parameters and aesthetic mappings used in geom_type functions see the ggplot2 documentation for individual geoms by using ?geom_type

`?`(geom_point)

or visit the ggplot2 documentations pages and cheatsheet

Facets

Split by 2 factors

Here we can plot the data with the Smokes variable as rows and Sex variable as columns.

facet_grid(Rows~Columns)

pcPlot <- ggplot(data = patients_clean, aes(x = Height, y = Weight, color = Sex)) + geom_point()
pcPlot + facet_grid(Smokes ~ Sex)

Split by 1 factor

To split by one factor we can apply the facet_grid() function ommiting the variable before the “~”" to facet along columns in plot.

facet_grid(~Columns)

pcPlot <- ggplot(data = patients_clean, aes(x = Height, y = Weight, color = Sex)) + geom_point()
pcPlot + facet_grid(~Sex)

Split by 1 factor

To split along rows in plot, the variable is placed before the “~.”.

facet_grid(Rows~.)

pcPlot <- ggplot(data = patients_clean, aes(x = Height, y = Weight, color = Sex)) + geom_point()
pcPlot + facet_grid(Sex ~ .)

facet_wrap()

The facet_wrap() function offers a less grid-based structure but is well suited to faceting data by one variable.

For facet_wrap() we follow as similar syntax to facet_grid().

pcPlot <- ggplot(data = patients_clean, aes(x = Height, y = Weight, color = Sex)) + geom_point()
pcPlot + facet_wrap(~Smokes)

Multiple variables

For more complex faceting both facet_grid and facet_wrap can accept combinations of variables. Here we use facet_wrap.

pcPlot <- ggplot(data = patients_clean, aes(x = Height, y = Weight, color = Sex)) + geom_point()
pcPlot + facet_wrap(~Pet + Smokes + Sex)

Multiple variables

Or in a nice grid format using facet_grid() and the Smokes variable against a combination of Gender and Pet.

pcPlot + facet_grid(Smokes ~ Sex + Pet)

Plotting Order

Plotting order in a boxplot

We will shortly discuss how to change various aspects of the plot layout and appearance. However, a common-asked question is how to change the order in which R plots a categorical variable. Consider the boxplot to compare weights of males and females:

ggplot(patients_clean, aes(x = Sex, y = Weight)) + geom_boxplot()

Plotting order and factors

Here, R decides the order to arrange the boxes according to the levels of the categorical variable. By default this is the alphabetical order. i.e. Female before Male.

summary(patients_clean$Sex)

##    Length     Class      Mode 
##       100 character character

Plotting order and factors

Depending on the message we want the plot to convey, we might want control over the order of boxes. The factor functions allows us to explictly change the order of the levels.

patients_clean$Sex <- factor(patients_clean$Sex, levels = c("Male", "Female"))
ggplot(patients_clean, aes(x = Sex, y = Weight)) + geom_boxplot()

Exercise set 1

Link_to_exercises

Link_to_answers

Scales

Scales and their legends have so far been handled using ggplot2 defaults. ggplot2 offers functionality to have finer control over scales and legends using the scale methods.

Scale methods are divided into functions by combinations of

the aesthetics they control.
the type of data mapped to scale.

scale_aesthetic_type

Try typing in scale_ then tab to autocomplete. This will provide some examples of the scale functions available in ggplot2.

Arguments

Although different scale functions accept some variety in their arguments, common arguments to scale functions include -

name - The axis or legend title
limits - Minimum and maximum of the scale
breaks - Label/tick positions along an axis
labels - Label names at each break

Controlling the X and Y scale.

Both continuous and discrete X/Y scales can be controlled in ggplot2 using:

scale_(x/y)_(continuous/discrete)

Continuous axes scales

In this example we control the continuous scale on the x-axis by providing a name, X-axis limits, the positions of breaks (ticks/labels) and the labels to place at breaks.

pcPlot + geom_point() + facet_grid(Smokes ~ Sex) + scale_x_continuous(name = "height ('cm')", limits = c(100, 
    200), breaks = c(125, 150, 175), labels = c("small", "justright", "tall"))

Discrete axes scales

Similary control over discrete scales is shown below.

pcPlot <- ggplot(data = patients_clean, aes(x = Sex, y = Height))
pcPlot + geom_violin(aes(x = Sex, y = Height)) + scale_x_discrete(labels = c("Women", "Men"))

Combining axes scales

Multiple X/Y scales can be combined to give full control of axis marks.

pcPlot <- ggplot(data = patients_clean, aes(x = Sex, y = Height, fill = Smokes))
pcPlot + geom_violin(aes(x = Sex, y = Height)) + scale_x_discrete(labels = c("Women", "Men")) + scale_y_continuous(breaks = c(160, 
    180), labels = c("Short", "Tall"))

Controlling other scales

When using fill, color, linetype, shape, size or alpha aesthetic mappings the scales are automatically selected for you and the appropriate legends created.

pcPlot <- ggplot(data = patients_clean, aes(x = Height, y = Weight, color = Sex))
pcPlot + geom_point(size = 4)

In the above example the discrete colors for the Sex variable was selected by default.

Manual discrete color scale

Manual control of discrete variables can be performed using scale_aes_Of_Interest_manual with the values parameter. Additionally in this example an updated name for the legend is provided.

pcPlot <- ggplot(data = patients_clean, aes(x = Height, y = Weight, color = Sex))
pcPlot + geom_point(size = 4) + scale_color_manual(values = c("Green", "Purple"), name = "Gender")

Colorbrewer for discrete color scale

Here we have specified the colors to be used (hence the manual) but when the number of levels to a variable are high this may be impractical and often we would like ggplot2 to choose colors from a scale of our choice.

The brewer set of scale functions allow the user to make use of a range of palettes available from colorbrewer.

Diverging

BrBG, PiYG, PRGn, PuOr, RdBu, RdGy, RdYlBu, RdYlGn, Spectral

Qualitative

Accent, Dark2, Paired, Pastel1, Pastel2, Set1, Set2, Set3

Sequential

Blues, BuGn, BuPu, GnBu, Greens, Greys, Oranges, OrRd, PuBu, PuBuGn, PuRd, Purples, RdPu, Reds, YlGn, YlGnBu, YlOrBr, YlOrRd

scale_color_brewer

pcPlot <- ggplot(data = patients_clean, aes(x = Height, y = Weight, color = Pet))
pcPlot + geom_point(size = 4) + scale_color_brewer(palette = "Set2")

## Warning: Removed 5 rows containing missing values (geom_point).

Colorbrewer palettes

For more details on palette sizes and styles visit the colorbrewer website and ggplot2 reference page.

Continuous Scales

Continuous scales

So far we have looked a qualitative scales but ggplot2 offers much functionality for continuous scales such as for size, alpha (transparancy), color and fill.

scale_alpha_continuous() - For Transparancy
scale_size_continuous() - For control of size.

alpha

Both these functions accept the range of alpha/size to be used in plotting.

Below the range of alpha to be used in plot is limited to between 0.5 and 1

pcPlot <- ggplot(data = patients_clean, aes(x = Height, y = Weight, alpha = BMI))
pcPlot + geom_point(size = 4) + scale_alpha_continuous(range = c(0.5, 1))

size

Below the range of sizes to be used in plot is limited to between 3 and 6

pcPlot <- ggplot(data = patients_clean, aes(x = Height, y = Weight, size = BMI))
pcPlot + geom_point(alpha = 0.8) + scale_size_continuous(range = c(3, 6))

Limits

The limits of the scale can also be controlled but it is important to note data outside of scale is removed from plot.

pcPlot <- ggplot(data = patients_clean, aes(x = Height, y = Weight, size = BMI))
pcPlot + geom_point() + scale_size_continuous(range = c(3, 6), limits = c(25, 40))

Labels

What points of scale to be labeled and labels text can also be controlled.

pcPlot <- ggplot(data = patients_clean, aes(x = Height, y = Weight, size = BMI))
pcPlot + geom_point() + scale_size_continuous(range = c(3, 6), breaks = c(25, 30), labels = c("Good", "Good but not 25"))

Color

Control of color/fill scales can be best achieved through the gradient subfunctions of scale.

scale_(color/fill)_gradient - 2 color gradient (eg. low to high BMI)
scale_(color/fill)_gradient2 - Diverging color scale with a midpoint color (e.g. Down, No Change, Up)

Both functions take a common set of arguments:-

low - color for low end of gradient scale
high - color for high end of gradient scale.
na.value - color for any NA values.

Color

An example using scale_color_gradient below sets the low and high end colors to White and Red respectively

pcPlot <- ggplot(data = patients_clean, aes(x = Height, y = Weight, color = BMI))
pcPlot + geom_point(size = 4, alpha = 0.8) + scale_color_gradient(low = "White", high = "Red")

Color

Similarly we can use the scale_color_gradient2 function which allows for the specification of a midpoint value and its associated color.

pcPlot <- ggplot(data = patients_clean, aes(x = Height, y = Weight, color = BMI))
pcPlot + geom_point(size = 4, alpha = 0.8) + scale_color_gradient2(low = "Blue", mid = "Black", high = "Red", 
    midpoint = median(patients_clean$BMI))

Labels

As with previous continuous scales, limits and custom labels in scale legend can be added.

pcPlot <- ggplot(data = patients_clean, aes(x = Height, y = Weight, color = BMI))
pcPlot + geom_point(size = 4, alpha = 0.8) + scale_color_gradient2(low = "Blue", mid = "Black", high = "Red", 
    midpoint = median(patients_clean$BMI), breaks = c(25, 30), labels = c("Low", "High"), name = "Body Mass Index")

Scales are very customizable

Multiple scales may be combined to create high customisable plots and scales

pcPlot <- ggplot(data = patients_clean, aes(x = Height, y = Weight, color = BMI, shape = Sex))
pcPlot + geom_point(size = 4, alpha = 0.8) + scale_shape_discrete(name = "Gender") + scale_color_gradient2(low = "Blue", 
    mid = "Black", high = "Red", midpoint = median(patients_clean$BMI), breaks = c(25, 30), labels = c("Low", 
        "High"), name = "Body Mass Index")

Transformations

Statistical transformations

In ggplot2 many of the statistical transformations are performed without any direct specification e.g. geom_histogram() will use stat_bin() function to generate bin counts to be used in plot.

An example of statistical methods in ggplot2 which are very useful include the stat_smooth() and stat_summary() functions.

Fitting lines

The stat_smooth() function can be used to fit a line to the data being displayed.

pcPlot <- ggplot(data = patients_clean, mapping = aes(x = Weight, y = Height))
pcPlot + geom_point() + stat_smooth()

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Loess and more

By default a “loess” smooth line is plotted by stat_smooth. Other methods available include lm, glm,gam,rlm.

pcPlot <- ggplot(data = patients_clean, mapping = aes(x = Weight, y = Height))
pcPlot + geom_point() + stat_smooth(method = "lm")

## `geom_smooth()` using formula 'y ~ x'

Fitting lines in groups

A useful feature of ggplot2 is that it uses previously defined grouping when performing smoothing.

If color by Sex is an aesthetic mapping then two smooth lines are drawn, one for each sex.

pcPlot <- ggplot(data = patients_clean, mapping = aes(x = Weight, y = Height, color = Sex))
pcPlot + geom_point() + stat_smooth(method = "lm")

## `geom_smooth()` using formula 'y ~ x'

Fitting lines in groups

This behaviour can be overridden by specifying an aes within the stat_smooth() function and setting inherit.aes to FALSE.

pcPlot <- ggplot(data = patients_clean, mapping = aes(x = Weight, y = Height, color = Sex))
pcPlot + geom_point() + stat_smooth(aes(x = Weight, y = Height), method = "lm", inherit.aes = F)

## `geom_smooth()` using formula 'y ~ x'

Summary statistics

Another useful method is stat_summary() which allows for a custom statistical function to be performed and then vizualised.

The fun parameter specifies a function to apply to the y variables for every value of x. In this example we use it to plot the quantiles of the Female and Male Height data

pcPlot <- ggplot(data = patients_clean, mapping = aes(x = Sex, y = Height)) + geom_jitter()
pcPlot + stat_summary(fun = quantile, geom = "point", color = "purple", size = 8)

Themes

Themes specify the details of data independent elements of the plot. This includes titles, background color, text fonts etc.

The graphs created so far have all used the default themes, theme_grey(), but ggplot2 allows for the specification of theme used.

Predefined themes

Predefined themes can be applied to a ggplot2 object using a family of functions theme_style()

Here is a scatter with the default theme…

pcPlot <- ggplot(data = patients_clean, mapping = aes(x = Weight, y = Height)) + geom_point()
pcPlot

…and the same scatter plot with the minimal theme.

pcPlot+theme_minimal()

Predefined themes

Several predifined themes are available within ggplot2 including:

theme_bw
theme_classic
theme_dark
theme_gray
theme_light
theme_linedraw
theme_minimal

Packages such as ggthemes also contain many useful collections of predined theme_style functions.

Custom themes

As well as making use of predefined theme styles, ggplot2 allows for control over the attributes and elements within a plot through a collection of related functions and attributes.

theme() is the global function used to set attributes for the collections of elements/components making up the current plot.

Within the theme functions there are 4 general graphic elements which may be controlled…

rect
line
text
title

…and 5 groups of related elements:

axis
legend
strip
panel (plot panel)
plot (Global plot parameters) ]

Custom themes

These elements may be specified by the use of their appropriate element functions including:

element_line()
element_text()
element_rect()

and additionally element_blank() to set an element to “blank”

Custom themes

A detailed description of controlling elements within a theme can be seen at the ggplot2 vignette and by typing ?theme into the console.

ggplot2 themes

Customizing your theme

To demonstrate customizing a theme, in the example below we alter one element of theme. Here we will change the text color for the plot.

Note because we are changing a text element we use the element_text() function.

A detailed description of which elements are available and their associated element functions can be found by typing ?theme.

pcPlot <- ggplot(data=patients_clean,
        mapping=aes(x=Weight,y=Height))+
  geom_point()
pcPlot+
  theme(
    text = element_text(color="red"),
    axis.text = element_text(color="red")
      )

Customizing your theme

If we wished to set the y-axis label to be at an angle we can adjust that as well.

pcPlot <- ggplot(data = patients_clean, mapping = aes(x = Weight, y = Height)) + geom_point()
pcPlot + theme(text = element_text(color = "red"), axis.text = element_text(color = "red"), axis.title.y = element_text(angle = 0))

Customizing your theme

Finally we may wish to remove axis line, set the background of plot panels to be white and give the strips (title above facet) a cyan background color.

pcPlot <- ggplot(data = patients_clean, mapping = aes(x = Weight, y = Height)) + geom_point() + facet_grid(Sex ~ 
    Smokes)
pcPlot + theme(text = element_text(color = "red"), axis.text = element_text(color = "red"), axis.title.y = element_text(angle = 0), 
    axis.line = element_line(linetype = 0), panel.background = element_rect(fill = "white"), strip.background = element_rect(fill = "cyan"))

Customizing your theme

Finally we may wish to remove axis line, set the background of plot panels to be white and give the strips (title above facet) a cyan background color.

Useful example for legend

A useful example of using the theme can be seen in controlling the legend. By default the legend is in right of plot.

pcPlot <- ggplot(data = patients_clean, aes(x = Height, y = Weight, color = Sex)) + geom_point()
pcPlot

Useful example for legend

By modifying the theme we can control the legend positioning.

pcPlot <- ggplot(data = patients_clean, aes(x = Height, y = Weight, color = Sex)) + geom_point()
pcPlot + theme(legend.position = "left")

Useful example for legend

We can control all aspects of a legend as we can for other theme elements.

pcPlot <- ggplot(data = patients_clean, aes(x = Height, y = Weight, color = Sex)) + geom_point()
pcPlot + theme(legend.text = element_text(color = "darkred"), legend.title = element_text(size = 20))

+ and %+replace%

When altering themes we have been using the + operator to add themes as we would adding geoms,scales and stats.

When using the + operator

Themes elements specified in new scheme replace elements in old theme
Theme elements in the old theme which have not been specified in new theme are maintained.

This makes the + operator useful for building up from old themes.

The + operator

In the example below, we maintain all elements set by theme_bw() but overwrite the theme element attribute of the color of text.

pcPlot <- ggplot(data = patients_clean, mapping = aes(x = Weight, y = Height)) + geom_point()
pcPlot + theme_bw() + theme(text = element_text(color = "red"))

The + operator

The consequence can be seen comparing the effect of theme() on a plot with a default theme or theme_minimal.

Since the default theme, theme_grey() contains a specification for axis.text color, I will not replace it with “+” operator.

pcPlot + theme(text = element_text(color = "red"))

pcPlot + theme_minimal() + theme(text = element_text(color = "red"))

%+replace%

In contrast %+replace% replaces all elements within a theme regardless of whether they have been previously specfied in old theme.

When using the %+replace% operator

Theme elements specified in new scheme replace elements in old theme
Theme elements in the old theme which have not been specified in new theme are also replaced by blank theme elements.

oldTheme <- theme_bw()

newTheme_Plus <- theme_bw() + theme(text = element_text(color = "red"))

newTheme_Replace <- theme_bw() %+replace% theme(text = element_text(color = "red"))

oldTheme$text

## List of 11
##  $ family       : chr ""
##  $ face         : chr "plain"
##  $ colour       : chr "black"
##  $ size         : num 11
##  $ hjust        : num 0.5
##  $ vjust        : num 0.5
##  $ angle        : num 0
##  $ lineheight   : num 0.9
##  $ margin       : 'margin' num [1:4] 0points 0points 0points 0points
##   ..- attr(*, "unit")= int 8
##  $ debug        : logi FALSE
##  $ inherit.blank: logi TRUE
##  - attr(*, "class")= chr [1:2] "element_text" "element"

newTheme_Plus$text

## List of 11
##  $ family       : chr ""
##  $ face         : chr "plain"
##  $ colour       : chr "red"
##  $ size         : num 11
##  $ hjust        : num 0.5
##  $ vjust        : num 0.5
##  $ angle        : num 0
##  $ lineheight   : num 0.9
##  $ margin       : 'margin' num [1:4] 0points 0points 0points 0points
##   ..- attr(*, "unit")= int 8
##  $ debug        : logi FALSE
##  $ inherit.blank: logi FALSE
##  - attr(*, "class")= chr [1:2] "element_text" "element"

newTheme_Replace$text

## List of 11
##  $ family       : NULL
##  $ face         : NULL
##  $ colour       : chr "red"
##  $ size         : NULL
##  $ hjust        : NULL
##  $ vjust        : NULL
##  $ angle        : NULL
##  $ lineheight   : NULL
##  $ margin       : NULL
##  $ debug        : NULL
##  $ inherit.blank: logi FALSE
##  - attr(*, "class")= chr [1:2] "element_text" "element"

+ and %+replace%

Original theme

+ and %+replace%

Theme modified with +

+ and %+replace%

Theme modified with %+replace%

This means that %+replace% is most useful when creating new themes.

theme_get and theme_set

In the examples we have shown you we have been modifying the theme for a specific plot. But once you have a theme that you really like you may want it to apply to every plot you draw.

The active theme is automatically applied to every plot you draw. Use theme_get to get the current theme, and theme_set to completely override it

Titles and Labels

Adding titles for plot and labels

So far no plot titles have been specified. Plot titles can be specified using the labs functions.

pcPlot <- ggplot(data = patients_clean, mapping = aes(x = Weight, y = Height)) + geom_point()
pcPlot + labs(title = "Weight vs Height", y = "Height (cm)")

Adding titles for plot and labels

You can also specify titles using the ggtitle and xlab/ylab functions.

pcPlot <- ggplot(data = patients_clean, mapping = aes(x = Height, y = Weight)) + geom_point()
pcPlot + ggtitle("Weight vs Height") + ylab("Height (cm)")

Saving Plots

Saving plots

Plots produced by ggplot can be saved in the same way as base plots

The ggsave() function allows for additional arguments to be specified including the type, resolution and size of plot.

By default ggsave() will use the size of your current graphics window when saving plots so it may be important to specify width and height arguments desired.

pcPlot <- ggplot(data = patients_clean, mapping = aes(x = Weight, y = Height)) + geom_point()
ggsave(pcPlot, filename = "anExampleplot.png", width = 15, height = 15, units = "cm")

Exercise set 2

Link_to_exercises

Link_to_answers

References

Contact

Any suggestions, comments, edits or questions (about content or the slides themselves) please reach out to our GitHub and raise an issue.

Introduction to ggplot2

Rockefeller University, Bioinformatics Resource Centre

http://rockefelleruniversity.github.io/Plotting_In_R/

Plotting in R with ggplot2

Graphics in R

ggplot2

Why use ggplot2

Grammar of Graphics

How ggplot2 builds a plot

Example scatter plot

What users are required to specify

Actual example scatter plot

Getting Started With ggplot2

Getting started with ggplot2

Our first ggplot2 graph

Our first ggplot2 graph

Our first ggplot2 graph

Our first ggplot2 graph

Our first ggplot2 graph

Our first ggplot2 graph

Our first ggplot2 graph

Our first ggplot2 graph

Our first ggplot2 graph

Geoms

Plot types

Line plots

Smoothed line plots

Bar plots

Histograms

Density plots

Box plots

GViolin plots

There are a world of geoms

Aesthetics

Aesthetics

Plot properties

Color

Point shape

Aesthetics in geom

Aesthetics in geom

Facets

Facets

Split by 2 factors

Split by 1 factor

Split by 1 factor

facet_wrap()

Multiple variables

Multiple variables

Plotting Order

Plotting order in a boxplot

Plotting order and factors

Plotting order and factors

Exercise set 1

Scales

Scales

Arguments

Controlling the X and Y scale.

Continuous axes scales

Discrete axes scales

Combining axes scales

Controlling other scales

Manual discrete color scale

Colorbrewer for discrete color scale

scale_color_brewer

Colorbrewer palettes

Continuous Scales

Continuous scales

alpha

size

Limits

Labels

Color

Color

Color

Labels

Scales are very customizable

Transformations

Statistical transformations

Fitting lines

Loess and more